Received 23 Oct 2015
|
Accepted 4 Aug 2016
|
Published 27 Sep 2016
Loss of RNA expression and allele-specific
expression associated with congenital heart
disease
David M. McKean
1,2
, Jason Homsy
1,2,3
, Hiroko Wakimoto
1
, Neil Patel
4
, Joshua Gorham
1
, Steven R. DePalma
1,5
,
James S. Ware
1,6,7
, Samir Zaidi
8
, Wenji Ma
9
, Nihir Patel
4
, Richard P. Lifton
8,10
, Wendy K. Chung
11
, Richard Kim
12
,
Yufeng Shen
9,13
, Martina Brueckner
8
, Elizabeth Goldmuntz
14
, Andrew J. Sharp
4,15
, Christine E. Seidman
1,2,5,
*,
Bruce D. Gelb
4,15,16,
* & J.G. Seidman
1
Congenital heart disease (CHD), a prevalent birth defect occurring in 1% of newborns, likely
results from aberrant expression of cardiac developmental genes. Mutations in a variety of
cardiac transcription factors, developmental signalling molecules and molecules that modify
chromatin cause at least 20% of disease, but most CHD remains unexplained. We employ
RNAseq analyses to assess allele-specific expression (ASE) and biallelic loss-of-expression
(LOE) in 172 tissue samples from 144 surgically repaired CHD subjects. Here we show that
only 5% of known imprinted genes with paternal allele silencing are monoallelic versus 56%
with paternal allele expression—this cardiac-specific phenomenon seems unrelated to CHD.
Further, compared with control subjects, CHD subjects have a significant burden of both LOE
genes and ASE events associated with altered gene expression. These studies identify
FGFBP2, LBH, RBFOX2, SGSM1 and ZBTB16 as candidate CHD genes because of significantly
altered transcriptional expression.
DOI: 10.1038/ncomms12824
OPEN
1Department of Genetics, Harvard Medical School, Boston, Massachusetts 02115, USA.2Cardiovascular Division, Brigham and Women’s Hospital, Harvard University, Boston, Massachusetts 02115, USA.3Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts 02114, USA. 4The Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, New York 10029, USA.5Howard Hughes Medical Institute, Harvard University, Boston, Massachusetts 02115, USA.6National Institute for Health Research Cardiovascular Biomedical Research Unit at Royal Brompton and Harefield National Health Service Foundation Trust and Imperial College London, London SW3 6NP, UK.7National Heart and Lung Institute, Imperial College London, London SW3 6NP, UK.8Department of Genetics, Yale University School of Medicine, New Haven, Connecticut 06510, USA.9Department of Systems Biology, Columbia University Medical Center, New York, New York 10032, USA.10Howard Hughes Medical Institute, Yale University, Connecticut 06510, USA.11Department of Pediatrics and Medicine, Columbia University Medical Center, New York, New York 10032, USA. 12Section of Cardiothoracic Surgery, University of Southern California Keck School of Medicine, Los Angeles, California 90089, USA.13Department of Biomedical Informatics, Columbia University Medical Center, New York, New York 10032, USA.14Department of Pediatrics, The Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA.15Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York 10029, USA.16Department of Pediatrics, Icahn School of Medicine at Mount Sinai, New York, New York 10029, USA. * These authors contributed equally to this work. Correspondence and requests for materials should be addressed to J.G.S.
C
ongenital heart disease (CHD)-causing mutations have
been identified in 450 genes including transcription
factors, signalling molecules
1–3and chromatin modifiers
4–8,
which direct the temporal and spatial expression of genes during
cardiac development. Recent studies have estimated that there are
B400 genes that can harbour loss or gain-of-function mutations
that cause CHD (denoted CHD genes)
4,8. We hypothesized that
other CHD genes could be identified by altered expression of one
(allele-specific expression (ASE)) or both alleles (loss-of-expression
(LOE); Fig. 1).
ASE occurs when transcription from one allele is selectively
silenced or enhanced, or when transcripts undergo selective
post-transcriptional degradation (for example, nonsense-mediated
decay; NMD). ASE occurs physiologically to control dosage
effects of chromosome X-encoded genes in females
9and to
silence the maternal or paternal allele of imprinted genes
10.
Transcription of one allele can be suppressed by allele-specific
chromatin marks
11, long noncoding RNAs
12or gene regulatory
element mutations
13. Other ASE studies include all genes where
one allele is expressed at a statistically higher level than the other
allele, an approach that estimates hundreds of ASE events per
tissue and thousands of ASE events per cell; however, this strategy
likely results in significant overestimates of ASE-event rates
14–16.
We studied ASE in discarded tissues from CHD patients,
hypothesizing that ASE events likely to cause CHD should result
in substantial allele bias in the expressed transcripts. Hence, we
focused on genes that are expressed in fetal heart that (1) are
normally biallelically expressed and (2) exhibit extreme ASE
(that is, 486% expression of one allele relative to the other).
Moreover, we suggest that ASE per se would not be enough to
cause a disease phenotype, particularly if dosage compensation
resulted in overall normal gene expression. Thus, we focused on
extreme ASE events, either with significantly altered gene
expression or in which a deleterious mutation was detected in
the expressed allele (Fig. 1) as candidate CHD genes.
Biallelic LOE (caused by inadequate trans-acting factors,
or combinations of gene regulatory mutations and/or NMD)
can cause CHD by either dominant or recessive mechanisms.
To identify LOE genes potentially responsible for CHD, we
focused on genes (1) that are highly expressed (upper quartile
of expressed genes), (2) with tightly regulated cardiovascular
expression and (3) with significantly downregulated (410-fold)
expression.
We demonstrate that 24% of extreme ASE events in CHD
subjects are associated with significantly altered levels of gene
expression (compared with 0% of extreme ASE events in control
subjects). We identify nine genes in CHD subjects that are
functionally null—three due to ASE with a damaging mutation in
the expressed allele and six due to biallelic LOE. We propose
FGFBP2, LBH, RBFOX2, SGSM1 and ZBTB16 as especially strong
candidate CHD genes.
Results
RNAseq expression analyses of CHD subjects and controls.
To identify cardiac gene expression, we studied 144 probands
(average age, 2.9 years; range, fetal to 21 years) enrolled in the
Pediatric Cardiac Genomics Consortium
17and performed RNA
sequencing (RNAseq) on 172 surgically discarded cardiovascular
tissues (Supplementary Data 1 and 2). We also studied gene
expression data from ‘normal’ adult (average age, 49.3 years;
range, 20–70 years) cardiac tissues (n ¼ 87, left ventricle; n ¼ 26,
right atria) from the Genotype-Tissue Expression Consortium
18(GTEx, n ¼ 95 subjects; Supplementary Data 3 and 4) and fetal
cardiac tissues without CHD (n ¼ 5, gestational age 15–16 weeks).
RNA expression was measured using standard RNAseq
procedures (see Methods). The 172 CHD samples were obtained
from eight different cardiovascular tissues (aorta, atrial septum,
ductus arteriosus, interventricular septum, left ventricle,
pulmon-ary artery, right atrium and right ventricle; Supplementpulmon-ary Data 2
and Supplementary Table 1) with at least six samples per tissue.
Approximately 15,300 expressed genes were expressed in each
sample (two or more aligned reads per million (r.p.m.); Fig. 2a
(grey bars) and Supplementary Table 1), and the average
number of genes expressed per subject per tissue ranged from
14,938 (interventricular septum) to 15,883 (pulmonary artery).
Expression analyses were performed by comparing single samples
to the mean of all other samples of the same tissue type
(for example, right atrial CHD sample versus all other right atrial
CHD samples).
Identification of extreme ASE events. To detect extreme ASE
events in genes that are normally biallelically expressed,
single-nucleotide polymorphisms (SNPs) were identified from whole
exome
4,8(WES, n ¼ 130) or genome (WGS, n ¼ 14) sequencing
of CHD probands and available unaffected parents. SNPs were
identified in GTEx donors from Illumina Exome and 5M SNP
arrays. At each genomic heterozygous SNP (see Methods), an
allele ratio (reference RNAseq reads/alternate RNAseq reads) was
computed (Supplementary Fig. 1). To focus on ASE events in
genes with normal biallelic expression, SNPs with typical biased
expression were excluded (see Methods, Supplementary Fig. 1
and Supplementary Table 2). SNPs were then phased using
parental genotypes or by assuming that the most highly expressed
base at each SNP is encoded by the same allele. Compound
allele ratios were then calculated for each gene as the ratio
Biallelic RNA expression gDNA
C C
C C
ASE, loss of expression (dominant)
C C
ASE, gain of expression (dominant)
C C
LOE (dominant or recessive)
C C
ASE, damaging mutation in expressed allele (recessive)
*
G T G T G T G T G TFigure 1 | Identification of extreme ASE genes in subjects with CHD. Shown are both alleles of a gene that differ by the SNP haploblocks ‘CC’ (blue) and ‘GT’ (red), as identified by WES, WGS or SNP-array genotyping. RNAseq analysis (read counts at heterozygous positions) reveals the expression of both alleles (biallelic RNA expression) or the disproportionate expression of one allele over another (ASE). RNAseq expression analyses (comparing each sample to the average of all other samples within a tissue group) identify relative loss and gain of expression. Variant analysis, in conjunction with RNAseq analysis, can further identify LOF mutations in the expressed allele (*).
of the summed reads corresponding to each allele (Methods).
This phasing methodology was 98% accurate in F1 mouse tissues
(M. musculus M. castaneus; Supplementary Table 3) and 100%
accurate in three human WGS trios (affected proband and
unaffected parents), when we applied a compound allele ratio
threshold Z7 (Supplementary Fig. 2c). Transcripts with a
compound allele ratio Z7.2 and a binomial P valueo0.01
(Bonferroni-corrected for the number of expressed genes with
heterozygous SNPs (Supplementary Data 2 and 4)) were
designated as having extreme ASE. Finally, both
over-represented ASE genes (that is, 45% of subjects including at
least one control subject have ASE events in the same gene;
Supplementary Table 4) and ASE events in genes with low fetal
heart expression (Supplementary Table 5) were removed.
DNA-sequencing methodology strongly influenced extreme
ASE detection (Fig. 2b). WGS and WES data yielded 5.1 and 1.4
extreme ASE events per CHD subject, respectively; SNP arrays
yielded 3.3 extreme ASE events per GTEx donor. In total,
we detected 607 extreme ASE events; 491 events in 17 known
imprinted genes
19(Supplementary Data 5 and www.geneimprint.
com) and 116 events (CHD, 56; GTEx, 60; Supplementary
Tables 6 and 7) in genes normally biallelically expressed. Dideoxy
sequencing of cDNAs from CHD tissues confirmed 17/17
ASE events in imprinted genes (100% validation rate), and
45/56 ASE events in non-imprinted genes (80% validation rate;
Supplementary Figs 3 and 4 and Supplementary Data 6).
Imprinting in cardiovascular tissues. Because parental gene
imprinting is a major cause of ASE in mammalian tissues, we
assessed imprinting in cardiovascular tissues. The set of genes
imprinted in fetal and/or neonatal cardiovascular tissues has not
been described. Forty-eight genes previously identified as being
imprinted in non-cardiac tissues were expressed in fetal heart and
also contained heterozygous SNPs. These were evaluated for ASE
in cardiovascular tissues. Seventeen genes had ASE in at least 50%
of CHD and GTEx subjects (Table 1), whereas 31 were
predominantly biallelically expressed (Supplementary Table 8).
Gene imprinting in mouse cardiovascular tissues was the same as
in human cardiovascular tissues except for three genes: CDKN1C,
IGF2R and SGCE. These three genes are biallelically expressed
in human hearts, but have ASE in mouse hearts. Biallelic
expression of these same genes in other human tissues has been
described
20–22. After excluding differences that were attributable
to
genotyping
methods,
ASE
of
imprinted
genes
was
indistinguishable in CHD proband tissues and GTEx tissues.
ASE events in known imprinted genes were excluded from
further analyses.
Extreme ASE is attributable to NMD in a minority of cases.
We identified 78 rare, nonsense mutations in genes that were
expressed at sufficient levels to evaluate ASE in CHD probands
(Supplementary Table 9). Only 14/78 (18%) genes exhibited
NMD and had significantly reduced expression of the allele
harbouring the LOF mutation, even after employing a less
stringent definition of ASE (allele bias 44). This low percentage
of NMD is consistent with previous reports
23. Unfortunately,
this analysis could not be performed on GTEx samples because
complete coding sequence data were unavailable for these
subjects.
Of the 45 extreme ASE events observed in CHD probands
(Supplementary Table 6), seven ASE events (Table 2) resulted
from nonsense (ASPN, CTSA, PGM1 and RBFOX2), splice site
(AARSD1) or frameshift (C7 and RETSAT) variants that caused
NMD. In sum, 38/45 (85%) extreme ASE events in CHD
probands remain unexplained and could potentially reflect
mutations in gene regulatory elements.
Extreme ASE genes have significant expression changes.
We hypothesized that genes whose expression levels differ
between subjects with extreme ASE versus subjects with biallelic
expression are candidate CHD genes. After excluding eight CHD
and three GTEx tissues that failed quality controls (Methods), we
compared gene expression in 37 extreme ASE genes in CHD
subjects and 57 extreme ASE genes in GTEx subjects to the mean
expression of biallelically expressed samples of the same tissue
type (for example, right atrial ASE expression compared with
right atrial biallelic expression). In CHD subjects, upregulated
ASE genes (fold 45, Po0.05, P calculated from z-score))
included MYOZ1 and FGFBP2 (observed in two subjects) and
downregulated ASE genes (fold
o0.65, Po0.05, P calculated
from z-score) included SGSM1, AARSD1, C5orf46, SDHB, CBR1
and RBFOX2 (Table 3). By contrast, no extreme ASE gene had
significantly different expression in GTEx subjects.
Identification of functionally null genes. We identified CHD
gene candidates who had extreme ASE and harboured a
dele-terious mutation in the expressed allele, and so are unlikely to
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 0 1 2 3 4 5 6 Subjects# Extreme ASE genes
CHD WGS CHD exome GTEx 0 5,000 10,000 15,000 20,000 CHD WGS CHD exome GTEx Mouse Genes expressed
Expressed genes w/ Het SNPs
a
b
Figure 2 | Extreme ASE genes preferentially identified in WGS subjects. Shown in a are the number of genes with a minimum expression of 2 r.p.m. (grey bars), and the number of expressed genes that contain heterozygous SNPs (black bars) for CHD WGS (n¼ 30 tissues) and CHD WES probands (n¼ 142 tissues), GTEx donors (n ¼ 113 tissues) and mouse C57Bl6/Castaneus F1 hybrids (n ¼ 7 tissues). s.d. is indicated. b The distribution of extreme ASE events per subject by genotyping platform. Extreme ASE events were identified in 470% of WGS subjects (n¼ 14). However, extreme ASE events were identified in onlyB20% of WES subjects (n ¼ 130) and B45% of GTEx donors (n ¼ 95).
make functional protein. Three extreme ASE genes, C17orf97,
CRACR2B and FGFBP2, encoded rare, putatively deleterious
variants in the expressed allele (Table 2). Although FGFBP2 has
relatively common ASE (45% of subjects (Supplementary
Table 6)), its functional null status makes it a
candidate-recessive CHD gene. These analyses could not be performed in
GTEx samples.
Biallelic LOE of genes that are typically both highly expressed
and tightly regulated are another class of functionally null genes
that may cause CHD. We identified genes with biallelic LOE (fold
o0.1, Po2.7 10
3(z-scoreo 3), P calculated from z-score)
in six CHD probands (Table 3) but none in GTEx donors
(P ¼ 7.8 10
3, Fisher Exact test). Five LOE genes, LBH, FRG1B,
PHKG1, IRX5 and ZBTB16, have no homozygous LOF variants
in the Exome Aggregation Consortium (ExAC) database
(exac.broadinstitute.org), while TRMT2B, an X-linked gene, is
hemizygous in a significant number of subjects. Significant
downregulation of all biallelic LOE genes was confirmed using
quantitative PCR (qPCR; Supplementary Table 10), although this
analysis identified only an approximately threefold reduction in
FRG1B and IRX5. Biallelic LOE of PHKG1 and IRX5 occurred in
two subjects both with Kabuki syndrome (MIM147920 (ref. 7)),
caused by damaging de novo KMT2D mutations
4.
Discussion
Our transcriptome analyses of tissues from CHD patients
identified several CHD gene candidates, including RBFOX2,
Table 1 | ASE of imprinted genes in cardiovascular tissues.
Gene Chr:Pos (hg19) CHD ASE* GTEx ASE* % ASE Coding/noncoding Expressed allele FHE Mouse
ZDBF2 chr2:207139522-207179148 4/7 15/15 86 C P 8.2 ASEw NAP1L5 chr4:89617065-89619023 9/10 14/14 96 C P 18.3 NE FAM50B chr6:3849631-3851551 4/5 22/23 93 C P 37.9 NE PLAGL1 chr6:144261436-144385735 16/16 33/34 98 C P 39.7 ASE PEG10 chr7:94285636-94299006 1/1z 0/0 100 C P 98.8 ASE MEST chr7:130126015-130146138 7/8 1/1 89 C P 47.7 ASE H19 IGF2 chr11:2016405-2170833 chr11:2016405-2170833 12/12 4/4 68/69 1/1 99 100 NC C M P 3287 2677 ASE ASE DLK1 MEG3 RTL1 chr14:101193201-101373305 chr14:101193201-101373305 chr14:101193201-101373305 17/17 5/5 1/1z 39/40 2/8 0/0 98 54 100 C NC C P M M 76.4 285.3 3.2 ASE ASE ASEw MAGEL2 NDN SNRPN SNURF chr15:23888695-25244225 chr15:23888695-25244225 chr15:23888695-25244225 chr15:23888695-25244225 2/2 55/55 60/60 0/0 0/0 NS 48/48 4/4 100 100 100 100 C C C C P P P P 3.9 63.8 68.9 99.1 ASEw,y ASE ASE ASE PEG3 chr19:57321444-57352094 14/15 14/17 88 C P 103.2 ASE NNAT chr20:36149606-36152090 1/2 0/0 50 C P 21.1 NS
ASE, allele-specific expression; CHD, congenital heart disease; FHE, fetal heart expression (reads per million aligned reads); GTEx, Genotype-Tissue Expression Consortium; M, maternal; NE, not expressed; NS, no SNP; P, paternal; SNP, single-nucleotide polymorphism.
*Number subjects with silenced allele/number of informative subjects. wMouse ASE observed in pulmonary artery.
zPEG10 and RTL1 are not expressed in postnatal cardiac tissues and are silenced in the only fetal subject in the study. yMouse ASE observed in skeletal muscle.
Table 2 | Extreme ASE genes with LOF variants in silenced allele or damaging variants in expressed allele.
Gene ID Mutation Predicted
effect
CADD score
ASE effect
MAF FHE PCGC cases LOF AF
PCGC controls LOF AF
ExAC LOF AF Rare LOF mutations in silenced allele-likely NMD
AARSD1 1-00384 chr17:41102746C4A Spl Acceptor* 24.2 NMD 4.1 10 5 138 0 0 0
RBFOX2w 1-05368 chr22:36155972G4A Nonsense 26.9 NMD 0 235.0 1.4 10 3 0 1.8 10 5
PGM1 1-01021 chr1:64100580G4T Nonsense 40 NMD 0 124.2 0 0 6.4 10 5
ASPN 1-05398 chr9:95228784G4A Nonsense 36 NMD 4.1 10 5 16.9 0 1.1 10 3 3.5 10 4
CTSA 1-01620 chr20:44522702C4A Nonsense ND NMD 0 122.3 0 2.7 10 4 6.9 10 4
C7 1-00070 chr5:40945362TACG TCGACAGA4T
Frameshift NA NMD 0 156.7 2.8 10 3 5.6 10 4 1.4 10 3 RETSAT 1-01024 chr2:85571195TCA4T Frameshift NA NMD 1.2 10 3 15.8 5.6 10 3 5.6 10 3 9.7 10 3 Rare damaging missense mutation in expressed allele-likely LOF
FGFBP2z 1-01984 chr4:15964134A4C p.Trp207Gly 15.41 Null 6.3 10 4 0y 0|| 0|| 1.6 10 5 || C17orf97 1-01485 chr17:260239C4T p.Arg30Trp 17.77 Null 3.5 10 4 16.8 0|| 0|| 0|| CRACR2B 1-04333 Chr11:829356G4A Spl Acceptorz NA Null 4.4 10 4 20.9 0|| 0|| 0||
AF, allele frequency; ASE, allele-specific expression; CADD, combined annotation-dependent depletion; CHD, congenital heart disease; ExAC, Exome Aggregation Consortium; FHE, fetal heart expression (reads per million aligned reads); LOF, loss-of-function; MAF, minor allele frequency in ExAC; NMD, nonsense-mediated decay; PA, pulmonary artery; PCGC, Pediatric Cardiac Genomics Consortium; PV, pulmonary valve.
*Consensus splice site mutation (within first two bases of intron). wMutation is de novo.
zStrong candidate CHD gene (identified in this study).
yFGFBP2 is not expressed in fetal heart, but is expressed in PA and PV.
||Frequency of homozygous (ExAC) or combined homozygous and compound heterozygous (PCGC cases and controls) LOF mutations. zNonconsensus splice site mutation.
a recently discovered definitive CHD gene
8. Although two LOE
genes, FRG1B and TRMT2B, have no known role in cardiac
development, the known functions of other candidate genes
increase the likelihood that altered expression could cause CHD.
In addition, we identified two novel findings related to
cardiovascular imprinted genes.
There were no significant differences in imprinting between
CHD and GTEx subjects, suggesting that imprinting defects are
not a common cause of CHD. However, we note the following
unexpected observations. First, the imprinted gene RTL1 has
maternal ASE in both human and mouse cardiovascular tissues
(Table 1 and Supplementary Table 11) but the opposite (paternal)
allele is expressed in other fetal and placental tissues
24,25.
Temporal and spatial-dependent parental allele switching has
been observed in only two other imprinted genes, GRB10 and
IGF2 (refs 26,27). Second, RTL1 is the only protein-coding,
imprinted gene with maternal ASE in cardiovascular tissues.
Imprinted genes in other tissues do not have a significant
maternal or paternal bias. In cardiac tissues, imprinted gene
clusters were significantly biased (P ¼ 0.008, Fisher Exact test) for
paternal ASE (11 ASE; 8 biallelic) versus maternal ASE (1 ASE; 12
biallelic). Only 5% (1/21) of maternally expressed, imprinted
genes had ASE, while 56% of paternally expressed, imprinted
genes (14/25) had ASE (P ¼ 3.1 10
4, Fisher Exact test).
Together, these data suggest a cardiac-specific mechanism that
removes imprinting ‘marks’ that would normally silence maternal
allele expression.
Although extreme ASE events were identified in both CHD
and GTEx subjects, ASE associated with significantly altered gene
expression was observed only in CHD subjects (9/37 CHD
subjects; 0/57 GTEx subjects; P ¼ 1.1 10
4, Fisher Exact test).
As genes with extreme ASE and downregulated expression could
phenocopy genes harbouring loss-of-function (LOF) mutations in
one allele (ignoring potential dominant-negative effects), we
compared the LOF allele frequency (AF) between CHD and
control data sets. Five of six ASE genes with reduced expression
are constrained and have a low frequency (o0.001) of LOF
mutations in the ExAC database (Table 3). Three of these,
AARSD1, SDHB and C5orf46, have no known functions in the
heart. The other two genes, RBFOX2 and SGSM1, are strong
candidates for contributing to CHD. Prior WES analyses
identified de novo RBFOX2 LOF variants in four CHD probands
(including the subject with ASE), and SGSM1 LOF variants in two
CHD probands (one each de novo and inherited), but no RBFOX2
or SGSM1 LOF variant in 1,800 controls
3,4,8. The LOF AF
was significantly higher in CHD probands for both RBFOX2
(CHD AF ¼ 1.4 10
3; ExAC AF ¼ 1.8 10
5; odds ratio
(OR) ¼ 78; P ¼ 6.6 10
5, Fisher Exact test) and SGSM1
(CHD AF ¼ 9.4 10
4; ExAC AF ¼ 9.1 10
5; OR ¼ 10.4;
P ¼ 0.02, Fisher Exact test) than observed in
B55,000 control
exomes. Moreover, the estimated odds ratio for SGSM1 is
conservative as we excluded both the subjects with ASE and
another CHD subject with markedly reduced biallelic expression
(1-02922: 0.19-fold, P ¼ 0.02, P calculated from z-score).
FGFBP2, another strong CHD gene candidate, is predicted to
lack all gene functions in two CHD subjects, through two
different mechanisms. One subject had extreme ASE with a
deleterious mutation in the expressed allele (Table 2), while the
other subject is predicted to have complete loss of FGFBP2 gene
function due to severely reduced biallelic expression (1-02697,
Table 3 | Extreme ASE and biallelic LOE events with significantly altered gene expression.
Gene ID Tissue Proband
Expr*
Mean Expr ±s.d. (n)
Fold P valuew PCGC cases LOF AF
PCGC controls LOF AF
ExAC LOF AF ASE genes with loss of allele expression
RBFOX2z,y 1-05368 DuctArt 189.1 296±44 (15) 0.64 1.6 10 2 1.4 10 3 0 1.8 10 5
SGSM1z,y 1-01019 RA 60.3 133±51 (18) 0.45 4.7 10 2 9.4 10 4 0 9.1 10 5 AARSD1 1-00384 IVS 65.9 108±12 (7) 0.61 3.0 10 4 0 0 0 C5orf46 1-00713 LV 7.7 42±17 (9) 0.18 4.4 10 2 0 0 9.1 10 5 SDHB C417-01 C417-01 IVS LV 169.6 124.0 302±25 (7) 224±46 (9) 0.56 0.55 o1.0 10 4 3.0 10 2 0 0 8.2 10 5 CBR1 CHD-1548 CHD-1548 CHD-1548 LA LV RA 12.9 15.1 10.6 29±6.0 (10) 25±3.0 (7) 26±3.4 (9) 0.44 0.60 0.41 6.1 10 3 8.0 10 4 o1.0 10 4 0 2.8 10 4 2.6 10 3
ASE genes with gain of allele expression
FGFBP2 1-01024 RA 5.6 1.0±1.7 (15) 5.48 6.7 10 3 9.5 10 4 5.6 10 4 4.5 10 4 FGFBP2 1-01984 LA 9.2 1.4±0.4 (3) 6.86 o1.0 10 4 9.5 10 4 5.6 10 4 4.5 10 4
MYOZ1 1-02697 RV 26.9 4.2±3.5 (16) 6.45 o1.0 10 4 0 2.7 10 4 3.5 10 4
Genes with loss of expression of both alleles
LBHy 1-03051 AO 5.4 55±15 (6) 0.10 8.0 10 4 0|| 0|| 0|| ZBTB16y 1-03316 AO 1.2 22±5.8 (6) 0.06 3.0 10 4 0|| 0|| 0|| IRX5 1-03948z RV 0.6 13±4.1 (20) 0.05 1.9 10 3 0|| 0|| 0|| PHKG1 1-00596z RA 2.6 30±8.7 (47) 0.09 1.5 10 3 0|| 0|| 1.6 10 5|| FRG1B 1-04119 PA 0.3 5.4±1.1 (10) 0.06 o1.0 10 4 0|| 0|| 0|| TRMT2B 1-02921 RV 0.0 5.5±1.3 (20) 0 o1.0 10 4 0|| 0|| 2.6 10 4|| PHKG1# 1-03948z RV 0.6 15±8.1 (20) 0.04 4.0 10 2 0|| 0|| 1.6 10 5|| FGFBP2#,y 1-02697 PV 0.5 14±3.4 (3) 0.04 1.0 10 4 0|| 0|| 1.6 10 5||
AF, allele frequency; AO, aorta; ASE, allele-specific expression; CHD, congenital heart disease; DuctArt, ductus arteriosus; ExAC, Exome Aggregation Consortium; IVS, interventricular septum; LA, left atrium; LOE, loss-of-expression; LOF, loss-of-function; LV, left ventricle; PA, pulmonary artery; PCGC, Pediatric Cardiac Genomics Consortium; PV, pulmonary valve; RA, right atrium; RV, right ventricle. *Expression in reads per million aligned reads.
wP value calculated from z-score. zIncludes de novo LOF mutations.
yStrong candidate CHD genes (identified in this study).
||Frequency of homozygous (ExAC) or combined homozygous and compound heterozygous (PCGC cases and controls) LOF mutations. zKMT2D de novo mutations identified in both 1-03948 (LOF) and 1-00596 (damaging-missense).
Table 3). Notably, FGFBP2 is a component of the FGF signalling
axis that regulates outflow tract and valve morphogenesis
28, and
both CHD subjects with abrogated FGFBP2 expression have
outflow tract defects.
Two probands with abnormal valve development had biallelic
LOE of LBH (limb bud and heart) or ZBTB16. LBH, a cardiac
developmental transcriptional co-activator, mediates neural crest
migration
29, which is required for aortic valve formation
30.
Overexpression of mouse Lbh produces valvular defects and
decreases Nppa (atrial natriuretic hormone) expression
31.
Consistent with this, LOE of LBH in proband 1-03051 was
associated
with
increased
NPPA
expression
(fold ¼ 12.3,
Po0.0001, P calculated from z-score). However, mice lacking
Lbh have no overt cardiovascular defects
32. ZBTB16 (also known
as PLZF) is a member of the Krueppel (C2H2-type) zinc-finger
transcription factor family that regulates expression of GATA4
(ref. 33); GATA transcription factors are important for outflow
tract development
34. ZBTB16 LOE in proband 1-03316 was
associated with reduced GATA4 expression (0.04-fold, P ¼ 0.19,
P calculated from z-score).
Kabuki syndrome is a complex developmental disorder including
CHD caused by mutation in KMT2D, a histone methyltransferase.
Our studies identified markedly reduced expression of PHKG1 in
two Kabuki syndrome subjects (Table 3) and decreased expression
of IRX5 in one subject. (IRX5 expression is normally low in right
atrial tissues, the only sample available from subject 1-00596.)
Damaging IRX5 mutations (MIM611174 (ref. 35)) cause CHD with
conduction abnormalities, marked frontonasal anomalies and
prominent ears, phenotypes that overlap that also occur in
Kabuki syndrome. On the basis of these data, we speculate that
KMT2D regulation of IRX5 and PHKG1 contributes to the
pathogenesis of Kabuki syndrome.
Although we identified nine ASE events likely related to CHD
in 144 probands, our analyses have several limitations. First, most
of the CHD tissues were acquired after birth, and genes with
aberrant expression that are developmentally downregulated
would escape detection. Second, only
B25% of cardiac expressed
genes contain heterozygous SNPs per sample, so a large fraction
of ASE genes cannot be detected by our methodology. We also
detected 3.6-fold more ASE genes in subjects genotyped with
WGS than subjects genotyped with WES. WES genotyping does
not evaluate untranslated region (UTR) sequences and, hence,
does not discover SNPs in these regions, resulting in low
efficiency of ASE detection in exome-genotyped samples. Finally,
to limit false-positives, we employed stringent definitions of ASE
(Z7.2 allele ratio; Pr0.01, Bonferroni-corrected binomial
distribution) and LOE (foldo0.1, Po2.7 10
3, P calculated
from z-score) genes, at the expense of under-calling expression
differences. We expect that cardiac RNA expression from more
CHD tissues will explain a larger proportion of disease.
We found that only a small subset of extreme ASE events was
attributable to NMD. Hence, unknown mechanisms accounted
for allele gain/loss of gene expression in the majority (85%) of
extreme ASE events identified in CHD subjects. While we
speculate that mutations in regulatory sequences may lead to
altered allele-specific transcription, another contributing factor
could be somatic mutations expressed in cardiovascular tissues,
but not blood (the DNA source for genomic sequencing), that
cause NMD.
In summary, integrated analyses of genomic DNA and RNAseq
in CHD cardiac tissues identified preferential silencing of
paternally expressed imprinted genes, several extreme ASE and
LOE genes relevant to cardiogenesis and potential downstream
targets of KMT2D. DNA sequence analyses of 81 trios identified
nine de novo mutations likely responsible for disease
4,8.
Assessment of RNAseq data from these and 63 singletons
identified seven instances (RBFOX2, SGSM1 (n ¼ 2), FGFBP2
(n ¼ 2), LBH and ZBTB16) with significantly reduced gene
expression likely contributing to CHD. These data support the
use of RNAseq analyses in identifying disease genes. We expect
that further study of WGS will identify damaging mutations in
regulatory elements that alter transcription of these CHD genes.
Methods
Patient and control cohorts
.
CHD probands were recruited from nine centres in the United States and the United Kingdom into the Congenital Heart Disease Genetic Network Study of the Pediatric Cardiac Genomics Consortium (CHD Genes: NCT01196182). The protocol was approved by the Institutional Review Boards of Boston Children’s Hospital, Brigham and Women’s Hospital, Great Ormond St Hospital, Children’s Hospital of Los Angeles, Children’s Hospital of Philadelphia, Columbia University Medical Center, Icahn School of Medicine at Mount Sinai, University of Rochester School of Medicine and Dentistry, Steven and Alexandra Cohen Children’s Medical Center of New York and Yale School of Medicine. Written informed consent was obtained from each participating subject or parent/guardian. Probands with CHD were selected based on availability of cardiovascular tissue and RNA quality (RNA integrity number, RIN). Cardiac diagnoses were obtained from review of echocardiogram, catheterization and operative reports; extracardiac findings were extracted from medical records. The control cohort consisted of RNAseq data from 113 heart tissues (left ventricle and/or right atrium) from 95 deceased subjects who were enroled in the GTEx programme. The GTEx data sets used for the analyses described in this manuscript were obtained from: dbGaP through dbGaP accession number phs000424.vN.pN on 02 August 2014.WES and WGS
.
Exomes of CHD probands were captured and sequenced at the Yale Center for Genome Analysis, as described4. In brief, gDNA isolated from venous blood was captured with the NimbleGen v2.0 exome capture reagent (Roche) and sequenced (Illumina HiSeq 2000, 75 base paired-end reads) to a mean read depth of 107. Reads were aligned to the hg19 reference genome using Novoalign (Novocraft), and variants called using HaplotypeCaller (Genotype Analysis Toolkit, GATK)36. Variants were filtered using the hard filters (FisherStrand (FS)o25, quality by depth (QD)o4) for passing variants. Identified heterozygous SNPs had a minimum genotype quality score of 50 and an allele balance (AB, number ALT reads/(number REF reads þ number ALT reads), where ‘ALT’ and ‘REF’ reads refers to reads containing the alternate or reference base in a heterozygous SNP) between 0.2 and 0.8.Whole genomes of 11 probands and three trios were sequenced to an average read depth of 35.2. gDNA libraries were made from 5 mg of purified DNA and sequenced on an Illumina HiSeq 2000 (101 base paired-end reads). Reads were aligned to reference genome hg19 using Novoalign (Novocraft) and variants were called using UnifiedGenotyper and filtered by VQSR (GATK36). Heterozygous SNPs were identified using the same criteria as for WES.
SNP array genotyping
.
GTEx subjects were genotyped on both Illumina Exome and Illumina 5M arrays.Variant annotation and minor allele frequency
.
Variants were annotated using SNPEff37. Damaging missense variants were predicted using both Polyphen2 (ref. 5) and CADD38. Minor AF (MAF) information for each SNP was extracted from the ExAC database, containing 455,000 individuals. If AF data were unavailable from ExAC, the maximum MAF reported in dbSNP, Exome Variant Server, HapMap or 1000 Genomes was chosen for subsequent calculations. RNAseq and analyses.
RNA was purified from RNAlater-treated frozen tissue, using Trizol (Life Technologies). RNA (RIN45) was converted into cDNA and into RNAseq libraries as described39. In brief, purified poly-A RNA that had gone through two rounds of oligo-dT selection was converted into cDNA and then made into RNAseq libraries. Libraries were sequenced (Illumina HiSeq 2000 or Illumina HiSeq 2500, 50-base paired-end reads) to a target depth of 420 million reads (median, 57 million reads; range, 20–530 million reads). Reads were aligned to the hg19 reference genome using TopHat 1.4 (using the following parameters: ‘-m 1 -a 5 --segment-mismatches 3 --segment-length 25 -g 0 --no-novel-juncs’, with splice junctions being defined by genes.gtf (Illumina iGenome download)). Mitochondrial and duplicate reads were discarded using Samtools and Picard’s MarkDuplicates, respectively. A median of 60% of reads was aligned to the reference genome, hg19, and 36% of reads uniquely aligned to the nuclear genome. Allele-specific reads were tallied using GATK UnifiedGenotyper at each heterozygous position identified by gDNA sequencing (using the following parameters: ‘--genotyping_mode GENOTYPE_GIVEN_ALLELES --alleles het_snps_only.vcf --output_mode EMIT_ALL_SITES’ where a personalized vcf file containing only heterozygous SNP was used as ‘het_snps_only.vcf’). Gene expression was determined by calculating reads per gene per million aligned reads (r.p.m.).Quality-control metrics for subjects
.
Some subjects were excluded from our study. Ignoring SNPs with expected ASE (chromosome X genes, previously reported imprinted genes, and SNPs within alternatively spliced exons), we observed that most heterozygous SNPs (with a minimum of 10 reads) were expressed biallelically (CHD exome (95.7±1.0%), CHD WGS (96.8±0.5%) and GTEx (97.6±0.6%; Supplementary Fig. 2a). Eight CHD and three GTEx subjects with substantially lower biallelic SNP expression (o75%; data not shown) were removed from this study.Quality-control metrics for SNPs
.
To ensure accurate ASE identification, all genotyped SNPs observed in RNAseq data were subjected to quality control. Only SNPs with at least five reads in RNAseq data or both alleles expressed were analysed for ASE.Low-quality genotype called SNPs
.
Low-quality SNPs observed in either WES or WGS generally reflected either misaligned DNA sequence reads because of gene orthologues, pseudogenes or other highly similar sequences. As previously described, low-quality SNPs either failed GATK variant filtration or had significantly biased AB. In addition, genes with a single heterozygous SNP, expressed at a high level (420 reads), with 100% monoallelic expression were excluded; the reference base of these SNPs was expressed in 480% of cases (Supplementary Fig. 2b), indicating a suspicious genotype.SNPs in alternatively spliced exons
.
Biased expression of SNPs in alternate exons could not be evaluated for ASE because we could not differentiate allele-specific expression versus allele-specific splicing. That is, SNPs (Supplementary Data 7, annotated ‘Filter_SNP_alt_splicing’) in exons that were only found in a subset of gene isoforms, as represented in three databases (RefGENE.txt (Illumina), UCSC Genes and Basic Gene Annotations Set (ENCODE/GENCODE)), were excluded. SNPs with suspected alignment biases.
Sequencing reads containing clustered SNPs (that is, those within 30 bp of two other SNPs) or SNPs in close proximity to an indel (within 30 bp of an indel) were often not aligned by TopHat/Bowtie introducing artefacts that appear as allele biases; these SNPs were excluded. In addition, SNPs in repeat regions (Supplementary Data 7, annotated‘Filter_SNP_duplicate_sequence’) were excluded if SNP and flanking sequences (50nt up- and downstream) aligned to multiple genomic locations (identified with BLAT (UCSC Genome Browser)) and if the ALT base was the REF base at one of those multiple locations.
SNPs biased in multiple subjects
.
Some ‘common’ biased SNPs (biased in 440% of CHD probands or GTEx subjects, with a minimum of three biased subjects) were present in genes with biallelically expressed SNPs. These common biased SNPs (except those likely to contribute to NMD) were filtered out as either not likely to impair cardiac development or as technical artifacts.Quality-control metrics for genes. Genes expressed in an allele-specific manner in many subjects
.
Excluded genes included all chromosomes X and Y genes, HLA- genes (that is, HLA-A, HLA-B, HLA-C, HLA-DMA, HLA-DMB, HLA-DOA, HLA-DOB, HLA-DPA1, HLA-DPB1, HLA-DQA1, HLA-DQA2, HLA-DQB1, HLA-DQB2, HLA-DRA, HLA-DRB1, HLA-DRB5, HLA-E, HLA-F, HLA-G, HLA-J, HLA-P and HLA-T) and noncoding genes. Coding genes were identified by SNPEFF_EFFECT designations: CODON_CHANGE_PLUS_ CODON_DELETION, CODON_CHANGE_PLUS_CODON_INSERTION, CODON_DELETION, CODON_INSERTION, FRAME_SHIFT, NON_ SYNONYMOUS_CODING, START_GAINED, STOP_GAINED,SYNONYMOUS_CODING, UTR_3_PRIME, UTR_5_PRIME. In addition, genes with ASE in 45% of subjects, including at least one GTEx subject, are unlikely to impair cardiac development; these ‘common’ ASE genes are reported in Supplementary Table 4.
RNAs with misaligned reads
.
Heterozygous SNPs were excluded if 420% of any other heterozygous SNPs in the same transcript were not expressed, or if a heterozygous coding SNP was not expressed. This pattern reflected misaligned reads.Genes with low fetal heart expression
.
RNAs that are unlikely to be involved in cardiac development (normalized fetal heart expressiono2 r.p.m.) were filtered out (Supplementary Table 5). This filter removed five and 24 ASE events from CHD probands and GTEx donors, respectively.Quality-control confirmation of extreme ASE. Allele bias observed in both aligned and unaligned reads (that is fastq files)
.
As noted above, TopHat/Bowtie alignment can introduce apparent allele bias into aligned RNAseq data. To confirm allele bias was not introduced by TopHat/Bowtie alignment, raw, unaligned sequencing reads containing: 10nt flanking the ALT/REF base, or 20nt either upstream or downstream of the ALT/REF base, or their reverse complements, were counted. The numbers of raw sequencing reads containing ALT and REF sequences were required to be similar to the numbers of ALT and REF aligned reads. Further, allele balances were required to be similar in both unaligned and aligned read counts.Visual inspection of RNAseq data in Integrative Genomics Viewer (Broad Institute)
.
Biased SNPs (Supplementary Data 7) were filtered out if other SNPs with biallelic expression were observed in the same gene—this was particularly important for CHD exome samples, which are not well genotyped in the 30UTRand for GTEx samples (in which genotyping was restricted to common SNPs). In addition, biased SNPs were excluded if visual inspection identified more than two
alleles. Thirty-nine SNPs (Supplementary Data 7: ‘Filter_SNP_complex_allele_ structure’) had multiple alleles likely due to misalignment of reads from pseudogenes and/or gene families.
Confirmation of ASE events by Sanger sequencing
.
At least one biased SNP per extreme ASE event was analysed using Sanger sequencing. PCR products derived from RNA were prepared from 200 ng total RNA by incubation with Superscript III Reverse Transcriptase (Thermo Fisher) and then cDNA was PCR-amplified with Phusion polymerase (New England Biolabs) using gene-specific primers (Supplementary Data 6) flanking the SNPs of interest. PCR products were gel-purified using the QIAquick Gel Extraction Kit (QIAgen) and Sanger sequenced (GENEWIZ, Boston). The relative peak heights of the ALT and REF alleles were measured. Extreme ASE events were confirmed when the relative peak height was 45.Quality controls removed REF allele bias
.
Unless the ALT base causes NMD, there should be no REF base versus ALT base expression bias in monoallelic SNPs. Before removal of ‘low-quality’ genome-wide SNPs, 87.1% of ASE events express the REF base, whereas after quality control,B50% of biased SNPs express the REF base (Supplementary Fig. 2b).Allele bias and ASE P value calculation
.
Allele bias and ASE P value were calculated for each SNP that passed quality-control measures. If there were multiple SNPs per gene, we either used phasing of SNPs (from maternal (mat) and paternal (pat) alleles) or we made the assumption that if there are multiple SNPs in a given gene, the expression bias will be unidirectional; that is, polymorphic bases with higher expression are on the same allele. Allele bias was calculated as follows:Reads containing heterozygous SNPs were counted and binned into one of four categories, based on inheritance and expression: (1) maternal inheritance, (2) paternal inheritance, (3) unknown inheritance with higher-allele expression and (4) unknown inheritance with lower-allele expression. For each heterozygous SNP in a gene, reads were summed into one of four allele categories (1) SNP-mat sum, (2) SNP-pat sum, (3) SNP-higher-allele sum and (4) SNP-lower-allele sum. These four allele categories were reduced to two, as follows:
If allele inheritance can be determined: If SNP-mat sum4SNP-pat sum
Allele bias ¼ (SNP-higher-allele sum þ SNP-mat sum)/(SNP-lower-allele sum þ SNP-pat sum)
If SNP-pat sum4SNP-mat sum
Allele bias ¼ (SNP-higher-allele sum þ SNP-pat sum)/(SNP-lower-allele sum þ SNP-mat sum)
If allele inheritance is unknown:
Allele bias ¼ SNP-higher-allele sum/SNP-lower-allele sum ASE P value is calculated using a binomial distribution model, and Bonferroni-corrected by the number of genes containing expressed heterozygous SNPs for each sample (Supplementary Data 2 and 4).
The assumption that the more highly expressed bases at heterozygous SNP positions are all on the same allele has the potential to introduce error into allele bias and statistical assessment of ASE. We directly tested this approach by studying F1 crosses of wild-type C57Bl6 and Castaneus mice. On the basis of parental mouse strain germline DNA sequence (Mouse Genomes Project; Wellcome Trust Sanger Institute), we estimated that F1 mice would have 18.5 million heterozygous SNPs, encoded within 19,236 transcripts. To make this more comparable to human WGS data, we only assessed every 18th heterozygous SNP. From RNAseq libraries prepared from left and right atrium, left and right ventricle, pulmonary artery, liver and skeletal muscle from P1 mice, we identified, on average, 14,678 expressed genes including 9,312 expressed genes with heterozygous SNPs (Fig. 2a). We then determined the false-positive rate of assigning unphased SNPs to alleles by comparing fully phased SNPs to unphased SNPs while varying the minimum allele bias (4 to 7-fold) and minimum read depth of monoallelic SNPs (4, 5, y 10) in each condition (Supplementary Fig. 2c). We determined that an allele bias of 7.2, with a minimum depth of five reads, yielded one false ASE event and 62 ‘true’ ASE events (1.59% false-positive rate). The false-positive rate associated with assigning SNPs to alleles was also assessed in three human WGS trios, using an allele bias of 7.2 and a minimum read depth of five. Fourteen ASE genes were called regardless of whether phasing was used to assign alleles, or whether phasing was ignored. Imprinted genes
.
Human genes previously identified as ‘imprinted’ in any tissue were identified in either of two online databases (http://www.geneimprint.com and www.otago.ac.nz/IGC). Genes with very low human fetal heart expression (o2 r.p.m.) were excluded because they were unlikely to contribute to normal heart development. Allele bias was calculated for each subject with heterozygous SNPs, and ASE events identified as described above.NMD analyses
.
NMD was assessed as described previously23. That is, the allele carrying the LOF mutation was at least fourfold less than the normal allele and read-depth sufficient to suggest a statistically significant (Po0.01) difference in allele ratio. P values were Bonferroni-corrected for the number of heterozygous nonsense mutations per subject (and not the number of expressed genes with heterozygous SNPs), so many more ASE events associated with NMD were identified than in our global ASE analyses.Expression analyses
.
We calculated both r.p.m. and reads per million aligned reads per kilobase of transcript per gene per sample. On average, 15,479 genes (range, 14,938–17,651) were expressed in cardiovascular tissues (Z2 r.p.m.; Fig. 1b and Supplementary Table 1). Each sample was compared with the average expression of all other samples of the same tissue type (fold change), and statistical significance was assessed by z-score. As quality control, tissue groups with more than four samples were included in the analyses. Samples with 4100 highly significant expression differences (foldo0.2, Po0.05 or fold 45, Po0.05, P calculated from z-score) were excluded from the analysis.RNA expression of ASE genes, which demonstrated significant downregulation (foldo0.65, Po0.05) or significant upregulation (fold 45, Po0.05), were based on the expected fold change ofB0.5 (allele loss of expression) and 47.2 (allele gain of expression), and relaxed by 30% because of the variability in gender, age and genotypes.
Fold downregulated: 0.5 þ (0.5 0.3) ¼ 0.65. Fold upregulated: 7.2 (7.2 0.3) ¼ 5.
For LOE analyses, we required a more stringent definition of significant downregulation (foldo0.1, Po2.7 10 3, P calculated from z-score). This P value is based on a z-scoreo 3.
Reported LOE events (Table 3) are limited to polyadenylated transcripts because RNAseq libraries were constructed from polyA-selected mRNA.
Quantitative RT–PCR
.
LOE events in CHD subjects, detected by RNAseq ana-lyses, were confirmed using qPCR. cDNA was prepared from 200 ng RNA from the CHD proband and at least two matched tissue samples using Superscript III Reverse Transcriptase (Thermo-Fisher). cDNA was PCR-amplified using Phusion polymerase (New England Biolabs) with SYBR green and gene-specific primers (Supplementary Table 12) and analysed using Fast Real-Time PCR (Applied Biosystems). Housekeeping gene (ACTB, GAPDH, GUSB and PPIA) expression was used to calculate DCt, and DDCt and P value (Student’s t-test) are reported in Supplementary Table 10.Data availability
.
Clinical and sequence data that support the findings of this study have been deposited in dbGaP under accession phs000571. The data that support the findings of this study are available from the corresponding author upon request.References
1. Gelb, B. D. & Chung, W. K. Complex genetics and the etiology of human congenital heart disease. Cold Spring Harb. Perspect. Med. 4, a013953 (2014). 2. Fahed, A. C., Gelb, B. D., Seidman, J. G. & Seidman, C. E. Genetics of
congenital heart disease: the glass half empty. Circ. Res. 112, 707–720 (2013). 3. Glessner, J. T. et al. Increased frequency of de novo copy number variants in
congenital heart disease by integrative analysis of single nucleotide polymorphism array and exome sequence data. Circ. Res. 115, 884–896 (2014). 4. Zaidi, S. et al. De novo mutations in histone-modifying genes in congenital
heart disease. Nature 498, 220–223 (2013).
5. Adzhubei, I. A. et al. A method and server for predicting damaging missense mutations. Nat. Methods 7, 248–249 (2010).
6. Vissers, L. E. et al. Mutations in a new member of the chromodomain gene family cause CHARGE syndrome. Nat. Genet. 36, 955–957 (2004). 7. Ng, S. B. et al. Exome sequencing identifies MLL2 mutations as a cause of
Kabuki syndrome. Nat. Genet. 42, 790–793 (2010).
8. Homsy, J. et al. De novo mutations in congenital heart disease with neurodevelopmental and other congenital anomalies. Science 350, 1262–1266 (2015).
9. Lyon, M. F. X chromosomes and dosage compensation. Nature 320, 313 (1986).
10. Peters, J. The role of genomic imprinting in biology and disease: an expanding view. Nat. Rev. Genet. 15, 517–530 (2014).
11. Chotalia, M. et al. Transcription is required for establishment of germline methylation marks at imprinted genes. Genes Dev. 23, 105–117 (2009). 12. Lee, J. T. & Bartolomei, M. S. X-inactivation, imprinting, and long noncoding
RNAs in health and disease. Cell 152, 1308–1323 (2013).
13. Fu, X. et al. Loss-of-function mutation in the X-linked TBX22 promoter disrupts an ETS-1 binding site and leads to cleft palate. Hum. Genet. 134, 147–158 (2015).
14. Kim, J. K., Kolodziejczyk, A. A., Illicic, T., Teichmann, S. A. & Marioni, J. C. Characterizing noise structure in single-cell RNA-seq distinguishes genuine from technical stochastic allelic expression. Nat. Commun. 6, 8687 (2015). 15. Consortium, G.T. Human genomics. The Genotype-Tissue Expression (GTEx)
pilot analysis: multitissue gene regulation in humans. Science 348, 648–660 (2015).
16. Deng, Q., Ramskold, D., Reinius, B. & Sandberg, R. Single-cell RNA-seq reveals dynamic, random monoallelic gene expression in mammalian cells. Science 343, 193–196 (2014).
17. Pediatric Cardiac Genomics Consortium et al. The Congenital Heart Disease Genetic Network Study: rationale, design, and early results. Circ. Res. 112, 698–706 (2013).
18. Consortium, G.T. The Genotype-Tissue Expression (GTEx) project. Nat. Genet. 45,580–585 (2013).
19. Morison, I. M., Paton, C. J. & Cleverley, S. D. The imprinted gene and parent-of-origin effect database. Nucleic Acids Res. 29, 275–276 (2001). 20. Matsuoka, S. et al. Imprinting of the gene encoding a human cyclin-dependent
kinase inhibitor, p57KIP2, on chromosome 11p15. Proc. Natl Acad. Sci. USA 93,3026–3030 (1996).
21. Kalscheuer, V. M., Mariman, E. C., Schepens, M. T., Rehder, H. & Ropers, H. H. The insulin-like growth factor type-2 receptor gene is imprinted in the mouse but not in humans. Nat. Genet. 5, 74–78 (1993).
22. Muller, B. et al. Evidence that paternal expression of the epsilon-sarcoglycan gene accounts for reduced penetrance in myoclonus-dystonia. Am. J. Hum. Genet. 71, 1303–1311 (2002).
23. MacArthur, D. G. et al. A systematic survey of loss-of-function variants in human protein-coding genes. Science 335, 823–828 (2012).
24. Morcos, L. et al. Genome-wide assessment of imprinted expression in human cells. Genome Biol. 12, R25 (2011).
25. Seitz, H. et al. Imprinted microRNA genes transcribed antisense to a reciprocally imprinted retrotransposon-like gene. Nat. Genet. 34, 261–262 (2003).
26. Baran, Y. et al. The landscape of genomic imprinting across diverse adult human tissues. Genome Res. 25, 927–936 (2015).
27. Blagitko, N. et al. Human GRB10 is imprinted and expressed from the paternal and maternal allele in a highly tissue- and isoform-specific fashion. Hum. Mol. Genet. 9, 1587–1595 (2000).
28. Zhang, J. et al. The FGF-BMP signaling axis regulates outflow tract valve primordium formation by promoting cushion neural crest cell differentiation. Circ. Res. 107, 1209–1219 (2010).
29. Jain, R. et al. Cardiac neural crest orchestrates remodeling and functional maturation of mouse semilunar valves. J. Clin. Invest. 121, 422–430 (2011). 30. Phillips, H. M. et al. Neural crest cells are required for correct positioning of the
developing outflow cushions and pattern the arterial valve leaflets. Cardiovasc. Res. 99, 452–460 (2013).
31. Briegel, K. J., Baldwin, H. S., Epstein, J. A. & Joyner, A. L. Congenital heart disease reminiscent of partial trisomy 2p syndrome in mice transgenic for the transcription factor Lbh. Development 132, 3305–3316 (2005).
32. Lindley, L. E. & Briegel, K. J. Generation of mice with a conditional Lbh null allele. Genesis 51, 491–497 (2013).
33. Wang, N. et al. Promyelocytic leukemia zinc finger protein activates GATA4 transcription and mediates cardiac hypertrophic signaling from angiotensin II receptor 2. PLoS ONE 7, e35632 (2012).
34. Laforest, B. & Nemer, M. GATA5 interacts with GATA4 and GATA6 in outflow tract development. Dev. Biol. 358, 368–378 (2011).
35. Bonnard, C. et al. Mutations in IRX5 impair craniofacial development and germ cell migration via SDF1. Nat. Genet. 44, 709–713 (2012).
36. McKenna, A. et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010).
37. Cingolani, P. et al. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly (Austin) 6, 80–92 (2012). 38. Kircher, M. et al. A general framework for estimating the relative pathogenicity
of human genetic variants. Nat. Genet. 46, 310–315 (2014). 39. Muehlschlegel, J. D. et al. Using next-generation RNA sequencing to
examine ischemic changes induced by cold blood cardioplegia on the human left ventricular myocardium transcriptome. Anesthesiology 122, 537–550 (2015).
Acknowledgements
We thank Anne Davis, Carolyn Westhoff, Paula Castano, Ana Cepin, Patricia Lanzano, Katrina Celis, Liyong Deng, Kelly Sadamistu and Nhu Tran for assistance with tissue collection. This work was supported by grants from the National Heart, Lung, and Blood Institute to the Pediatric Cardiac Genomics Consortium (U01-HL098188, U01-HL098147, U01-HL098153, U01-HL098163, U01-HL098123 and U01-HL098162) and the Cardiovascular Development Consortium (2UM1-HL098166) and the Howard Hughes Medical Institute (R.P.L. and C.E.S.), and the John S. LaDue Cardiovascular Fellowship (J.H.) and an Alan Lerner Research Award (J.H.). The views expressed are those of the authors and do not necessarily reflect those of the National Heart, Lung, and Blood Institute or the National Institutes of Health.
Author contributions
C.E.S., J.G.S. and D.M.M. conceived the study. J.H., S.R.D., J.S.W., S.Z., Y.S. and D.M.M. analysed exome variants. S.R.D. and D.M.M. analysed whole-genome variants. J.G. and D.M.M. made RNAseq libraries. H.W. and D.M.M. performed mouse work. Ne.P. and
D.M.M. developed RNAseq, ASE and LOE pipelines and analysed ASE data. W.M. and Ni.P. supported data analysis. W.K.C. and R.K. provided human heart tissues. R.P.L., W.K.C., R.K., Y.S., M.B., E.G., A.J.S., Ne.P. and J.H. edited the manuscript. C.E.S., B.D.G., J.G.S. and D.M.M. wrote the manuscript.
Additional information
Supplementary Informationaccompanies this paper at http://www.nature.com/ naturecommunications
Competing financial interests:The authors declare no competing financial interests. Reprints and permissioninformation is available online at http://npg.nature.com/ reprintsandpermissions/
How to cite this article:McKean, D. M. et al. Loss of RNA expression and allele-specific expression associated with congenital heart disease. Nat. Commun. 7:12824
doi: 10.1038/ncomms12824 (2016).
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